Given demand models of multiple products, the role of optimization is to find the best price strategy.

This paper is concerned with personalized pricing models aimed at maximizing the expected revenues or profits for a single item. While it is essential for personalized pricing to predict the purchase probabilities for each consumer, these predicted values are inherently subject to unavoidable errors that can negatively impact the realized revenues and profits. To address this issue, we focus on robust optimization techniques that yield reliable solutions to optimization problems under uncertainty. Specifically, we propose a robust optimization model for personalized pricing that accounts for the uncertainty of predicted purchase probabilities. This model can be formulated as a mixed-integer linear optimization problem, which can be solved exactly using mathematical optimization solvers. We also develop a Lagrangian decomposition algorithm combined with line search to efficiently find high-quality solutions for large-scale optimization problems. Experimental results demonstrate the effectiveness of our robust optimization model and highlight the utility of our Lagrangian decomposition algorithm in terms of both computational efficiency and solution quality.

This paper deals with price optimization, which is to find the best pricing strategy that maximizes revenue or profit, on the basis of demand forecasting models. Though recent advances in regression technologies have made it possible to reveal price-demand relationship of a large number of products, most existing price optimization methods, such as mixed integer programming formulation, cannot handle tens or hundreds of products because of their high computational costs. To cope with this problem, this paper proposes a novel approach based on network flow algorithms. We reveal a connection between supermodularity of the revenue and cross elasticity of demand. On the basis of this connection, we propose an efficient algorithm that employs network flow algorithms. The proposed algorithm can handle hundreds or thousands of products, and returns an exact optimal solution under an assumption regarding cross elasticity of demand. Even if the assumption does not hold, the proposed algorithm can efficiently find approximate solutions as good as other state-of-the-art methods, as empirical results show.

Price optimization seeks to identify the ideal price that will assist businesses to attract customers, optimizing sales, and enhancing profitability. Price optimization is the process of determining the best price for a product or service. Price optimization enables organizations to make informed decisions based on client and market data to determine the most efficient price point. Businesses can price their product or service to attract clients, maximizing deals or profitability, by using data rather than assumptions. Price optimization works best when viewed in a broader context, taking into account different factors when deciding on pricing.

New York, NY, May 26, 2021 (GLOBE NEWSWIRE) -- Facts and Factors have published a new research report titled "Artificial Intelligence in Retail Market By Type (Offline, and Online), By Technology (Natural Language Processing, Machine Learning, and Deep Learning, and Others), By Solution (Customer Relationship Management, Payment Services management, Price Optimization, Product Recommendation, and Planning, Supply chain management and Demand Planning, Virtual Assistant, Visual Search, Others) By Service (Managed Services, and Professional Services), By Deployment Model (On-Premises, and Cloud), and By Application (In-Store Visual Monitoring and Surveillance, Location-Based Marketing, Market Forecasting, Predictive Merchandising, Programmatic Advertising, and Others): Global Industry Perspective, Comprehensive Analysis, and Forecast, 2020 – 2026". "According to the research report, the global Artificial Intelligence in Retail Market was estimated at USD 2.7 Billion in 2019 and is expected to reach USD 20.05 Billion by 2026. The global Artificial Intelligence in Retail Market is expected to grow at a compound annual growth rate (CAGR) of 39% from 2020 to 2026". Digitalization in retail is much more than just linking objects. It's about turning data into observations that guide decisions that produce better market results.

In this paper, by leveraging abundant observational transaction data, we propose a novel data-driven and interpretable pricing approach for markdowns, consisting of counterfactual prediction and multi-period price optimization. Firstly, we build a semi-parametric structural model to learn individual price elasticity and predict counterfactual demand. This semi-parametric model takes advantage of both the predictability of nonparametric machine learning model and the interpretability of economic model. Secondly, we propose a multi-period dynamic pricing algorithm to maximize the overall profit of a perishable product over its finite selling horizon. Different with the traditional approaches that use the deterministic demand, we model the uncertainty of counterfactual demand since it inevitably has randomness in the prediction process. Based on the stochastic model, we derive a sequential pricing strategy by Markov decision process, and design a two-stage algorithm to solve it. The proposed algorithm is very efficient. It reduces the time complexity from exponential to polynomial. Experimental results show the advantages of our pricing algorithm, and the proposed framework has been successfully deployed to the well-known e-commerce fresh retail scenario - Freshippo.

We study the interplay of fairness, welfare, and equity considerations Studying the case of personalized pricing is conceptually challenging in personalized pricing based on customer features. Sellers because prices are a shared tool in drastically different are increasingly able to conduct price personalization based on domains: we consider lending/insurance, consumer goods, and public predictive modeling of demand conditional on covariates: setting provision. A crucial distinction is between value-based pricing customized interest rates, targeted discounts of consumer goods, that offers different prices to customers based on their estimated and personalized subsidies of scarce resources with positive externalities willingness to pay, and risk-based pricing which offers different like vaccines and bed nets. These different application areas prices to customers based on their estimated costs, as in lending may lead to different concerns around fairness, welfare, and equity and insurance [34]. While discrimination law is strongest in insurance on different objectives: price burdens on consumers, price envy, and lending, in lending, discrimination concerns often firm revenue, access to a good, equal access, and distributional consequences arise from individual agents providing offers from an actuariallyfair when the good in question further impacts downstream securitized rate sheet [9]. In particular, distributional concerns outcomes of interest. We conduct a comprehensive literature review regarding price optimization reflect overall concern for differentially in order to disentangle these different normative considerations adept/prepared/educated negotiating customers in insurance and propose a taxonomy of different objectives with mathematical and lending, but slight optimism in value-based pricing since lowincome definitions. We focus on observational metrics that do not assume individuals may be more price-sensitive [9]. Hence, the access to an underlying valuation distribution which is either unobserved majority of our analysis will focus on value-based pricing, which due to binary feedback or ill-defined due to overriding lends itself more readily to price optimization.

There are several techniques in use in various stages if maturity in retail and e-commerce. Many different tools and techniques feed into AI powered price optimization for retailers. When used together these can drive very significant top line and bottom-line results for retailers, and allow them to be much more agile in their response to changes in market conditions like competition, costs, inventory levels, and more. Here are the top 10 "need to know" concepts in the use of AI in price optimization for retailers. The first key in optimizing pricing is understanding how to group together like products, stores, and potentially customers.

Predictive opportunity scoring, predictive lead scoring, predictive analytics for forecast ... [ ] management and guided selling are the top four AI-based technologies B2B selling organizations plan to deploy in the next 12 months, according to Gartner. Sales organizations are under increased pressure to reduce selling costs while stabilizing margins and closing only the most profitable deals. Marketing teams across all industries are under increased pressure to increase the quantity, quality and qualification levels of leads that deliver the highest probability of closing this year. AI-based price and revenue management applications and platforms are proving indispensable in keeping sales, marketing, operations, services, accounting and senior management synchronized with real-time updates to achieve more. McKinsey's Global AI Survey: AI proves its worth, but few scale impact survey provides insights into where AI is making its greatest contributions and reducing expenses.

With the rapid growth in the fashion e-commerce industry, it is becoming extremely challenging for the E-tailers to set an optimal price point for all the products on the platform. By establishing an optimal price point, they can maximize overall revenue and profit for the platform. In this paper, we propose a novel machine learning and optimization technique to find the optimal price point at an individual product level. It comprises three major components. Firstly, we use a demand prediction model to predict the next day demand for each product at a certain discount percentage. Next step, we use the concept of price elasticity of demand to get the multiple demand values by varying the discount percentage. Thus we obtain multiple price demand pairs for each product and we have to choose one of them for the live platform. Typically fashion e-commerce has millions of products, so there can be many permutations. Each permutation will assign a unique price point for all the products, which will sum up to a unique revenue number. To choose the best permutation which gives maximum revenue, a linear programming optimization technique is used. We have deployed the above methods in the live production environment and conducted several AB tests. According to the AB test result, our model is improving the revenue by 1 percent and gross margin by 0.81 percent.